We use the context aggregation network (CAN) [1]} with different dilation rates (here we set 1, 2, 4) to obtain multi-scale 64-channel features \(\Phi _{1}\)  and \( \Phi _{2}\)  without losing spatial resolution. Considering the degradation of the spatial properties of the deep features extracted by CAN, we only utilize a channel attention block. By extending the non-local attention mechanism [2]} to multiple heterogeneous inputs, we propose a cross channel attention module (CCAM) to learn the difference between \(X_1\)  and the reference \(X_2\)  by establishing associations between \(\Phi _{1}\)  and \(\Phi _{2}\)  in latent space. The \(\Phi _{2}\)  and \(\Phi _{1}^{channel}\)  obtained by CCAM are concatenated and then fed into the Transformer decoder.
